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Data fusion is the process of combining data from multiple sources, allowing for a more complete and accurate assessment of a system or an environment than could have been otherwise provided by a single source. This paper considers and empirically evaluates a decision-level data fusion method for enabling reliable ocean turbine state detection based on data from multiple sensors. This method involves...
More than a decade of research has produced numerous representations and similarity measures to support time series classification and clustering. Yet most of the work in the field is so focused on the representation or similarity measure that it ignores the possibility of improving performance using ensembles of representations or classifiers. This paper explores ways of exploiting representational...
This paper investigates application of novel Bidirectional Data Partitioning Technique (BDP) to cancer survival analysis. Author has developed this technique for classification problems with unstable feature relevance and SEER Cancer Data illustrates this machine learning concept. BDP is applied for survival analysis in order to find groups of patients with different key factors that determine survival...
Boosting methods have been successfully applied in a wide variety of machine learning applications. In the context of data quality issues, a number of variants of the standard boosting method have been proposed and evaluated. To address the problem of mislabeled examples, ORBoost was developed to prevent over fitting to noisy examples. Our research group has recently proposed RUSBoost as an enhancement...
Ensembles of neural networks have been the focus of extensive studies over the past two decades. Effectively encouraging diversity remains a key element in yielding improved performance from such ensembles. Negatively correlated learning (NCL) has emerged as a promising framework for concurrently training an ensemble of learners while emphasizing the cooperation among them. The NCL methodology relies...
Prediction of O-linked glycosylation sites in proteins is a challenging problem. In this paper, we introduced a new method to predict glycosylation sites in proteins. First, we built a Markov random field (MRF) to represent the sequence position relationship and model the underlying distribution of glycosylation sites. We then considered glycosylation site prediction as a class imbalance problem and...
In cluster analysis, finding out the number of clusters, K, for a given dataset is an important yet very tricky task, simply because there is often no universally accepted correct or wrong answer for non-trivial real world problems and it also depends on the context and purpose of a cluster study. This paper presents a new hybrid method for estimating the predominant number of clusters automatically...
Recently, a novel "completely automated public Turing test to tell computers and humans apart (CAPTCHA)'' system has been proposed, in which users are asked to separate natural faces of humans and artificial faces of virtual world avatars. The system is based on the assumption that computers cannot separate them while it is an easy task for humans. Conventional digital forensics approaches to...
Convolutional neural network models have covered a broad scope of computer vision applications, achieving competitive performance with minimal domain knowledge. In this work, we apply such a model to a task designed to deter automated systems. We trained a convolutional neural network to distinguish between images of human faces from computer generated avatars as part of the ICMLA 2012 Face Recognition...
Captchas are frequently used on the modern world wide web to differentiate human users from automated bots by giving tests that are easy for humans to answer but difficult or impossible for algorithms. As artificial intelligence algorithms have improved, new types of Captchas have had to be developed. Recent work has proposed a new system called Avatar Captcha, in which a user is asked to distinguish...
Ensemble feature selection is known for its robustness and generalization of highly accurate predictive models. In this paper, we use different filter-based feature selection methods in an ensemble manner to improve face recognition. The goal is to distinguish human faces from avatar faces. Our approach was able to achieve very high accuracy, 99%, using less than 1% of the pixels in each image. This...
The contribution describes the application of the Team 'Computational Intelligence Group' from the University of Applied Sciences Mittweida (Germany) to the ICMLA Face Recognition Challenge 2012. In particular we explain the data preprocessing and feature extraction, which was applied before classification learning. Further we give details about the used classification algorithm - the enhanced generalized...
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